The saying "garbage in, garbage out" oversimplifies the complex issue of data quality, suggesting that poor data inevitably leads to poor outcomes without considering the sophisticated tools available to manage and assess data quality today. This phrase is often used to dismissively explain issues in data processing, ignoring the nuanced and technical approaches used to address data quality. Quality checks, which assess data across dimensions like accuracy, completeness, consistency, reliability, timeliness, uniqueness, usefulness, and differences, provide a structured methodology for understanding and improving data. These checks enable more precise discussions about data quality by quantifying various metrics, thus allowing data engineers to better determine what data is valuable, what needs fixing, and what should be avoided.